The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Machine Learning-Based Drone Detection and Classification: State-of-the-Art in Research
Open accessAbstract
This paper presents a comprehensive review of current literature on drone detection and classification using machine learning with different modalities. This research area has emerged in the last few years due to the rapid development of commercial and recreational drones and the associated risk to airspace safety. Addressed technologies encompass radar, visual, acoustic, and radio-frequency sensing systems. The general finding of this study demonstrates that machine learning-based classification of drones seems to be promising with many successful individual contributions. However, most of the performed research is experimental and the outcomes from different papers can hardly be compared. A general requirement-driven specification for the problem of drone detection and classification is still missing as well as reference datasets which would help in evaluating different solutions.
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Citations
Cites
Graph-based approach for airborne light detection and ranging segmentation
Vilariño, D. L., Cabaleiro, J. C., Martínez, J., Rivera, F. F., & Pena, T. F. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.015020 · Cited by 1,721
Cognitive radar: a way of the future
Haykin, S. (2006) · IEEE Signal Processing Magazine · doi:10.1109/msp.2006.1593335 · Cited by 1,229
Detecting Flying Objects Using a Single Moving Camera
Rozantsev, A., Lepetit, V., & Fua, P. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2564408 · Cited by 270
Classification of small UAVs and birds by micro-Doppler signatures
Molchanov, P., Harmanny, R. I. A., de Wit, J. J. M., Egiazarian, K., & Astola, J. (2014) · International Journal of Microwave and Wireless Technologies · doi:10.1017/s1759078714000282 · Cited by 261
Drone Classification Using Convolutional Neural Networks With Merged Doppler Images
Kim, B. K., Kang, H.-S., & Park, S.-O. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2016.2624820 · Cited by 234
Micro-UAV Detection and Classification from RF Fingerprints Using Machine Learning Techniques
Ezuma, M., Erden, F., Anjinappa, C. K., Ozdemir, O., & Guvenc, I. (2019) · 2019 IEEE Aerospace Conference · doi:10.1109/aero.2019.8741970 · Cited by 228
Using deep networks for drone detection
Aker, C., & Kalkan, S. (2017) · 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2017.8078539 · Cited by 202
Investigating Cost-effective RF-based Detection of Drones
Nguyen, P., Ravindranatha, M., Nguyen, A., Han, R., & Vu, T. (2016) · Proceedings of the 2nd Workshop on Micro Aerial Vehicle Networks, Systems, and Applications for Civilian Use · doi:10.1145/2935620.2935632 · Cited by 193
Micro-Doppler based detection and tracking of UAVs with multistatic radar
Hoffmann, F., Ritchie, M., Fioranelli, F., Charlish, A., & Griffiths, H. (2016) · 2016 IEEE Radar Conference (RadarConf) · doi:10.1109/radar.2016.7485236 · Cited by 189
A study on detecting drones using deep convolutional neural networks
Saqib, M., Daud Khan, S., Sharma, N., & Blumenstein, M. (2017) · 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2017.8078541 · Cited by 177
Cited by
A Comprehensive Review of Recent Research Trends on Unmanned Aerial Vehicles (UAVs)
Telli, K., Kraa, O., Himeur, Y., Ouamane, A., Boumehraz, M., Atalla, S., & Mansoor, W. (2023) · Systems · doi:10.3390/systems11080400 · Cited by 331
Isaac-Medina, B. K. S., Poyser, M., Organisciak, D., Willcocks, C. G., Breckon, T. P., & Shum, H. P. H. (2021) · 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw54120.2021.00142 · Cited by 9
Metadata
- Authors
- Taha, B., & Shoufan, A.
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 138669-138682
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2976792973
- Cited by
- 375
- References
- 31
- FWCI
- 15.31
- Type
- journal article
- Language
- en
- Open access
- gold
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
